Ensembles of distributed, heterogeneous resources, also known as Computational Grids are emerging as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic Grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in dynamic, heterogeneous, multi-user Grid environments. In this paper, we discuss the AppLeS project and outline our results.

In this paper, we consider the problem of allocating a large number of independent, equal-sized tasks to a heterogeneous "grid" computing platform. We use a non-oriented graph to model a grid, where resources can have different speeds of computation and communication, as well as different overlap capabilities. We show how to determine the optimal steady-state scheduling strategy for each processor (the fraction of time spent computing and the fraction of time spent communicating with each neighbor). This result holds for a quite general framework, allowing for cycles and multiple paths in the interconnection graph, and allowing for several masters. Because

In this paper, we consider the problem of allocating a large number of independent, equalsized tasks to a heterogenerous "grid" computing platform. Such problems arise in collaborative computing eorts like SETI@home. We use a tree to model a grid, where resources can have dierent speeds of computation and communication, as well as dierent overlap capabilities. We dene a base model, and show how to determine the maximum steady-state throughput of a node in the base model, assuming we already know the throughput of the subtrees rooted at the node's children. Thus, a bottom-up traversal of the tree determines the rate at which tasks can be processed in the full tree. The best allocation is bandwidth-centric: if enough bandwidth is available, then all nodes are kept busy; if bandwidth is limited, then tasks should be allocated only to the children which have suciently small communication times, regardless of their computation power. We then show how nodes with other capabilities ones that allow more or less overlapping of computation and communication than the base model can be transformed to equivalent nodes in the base model. We also show how to handle a more general communication model. Finally, we present simulation results of several demand-driven task allocation policies that show that our bandwidth-centric method obtains better results than allocating tasks to all processors on a rst-come, rst serve basis. Key words: heterogeneous computer, allocation, scheduling, grid, metacomputing. Corresponding author: Jeanne Ferrante The work of Larry Carter and Jeanne Ferrante was performed while visiting LIP. 1 1

In this paper, we consider steady-state scheduling techniques for heterogeneous systems, such as clusters and grids. We advocate the use of steady-state scheduling to solve a variety of important problems, which would be too difficult to tackle with the objective of makespan minimization. We give a few successful examples before discussing the main limitations of the approach.

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In this paper, we consider the problem of allocating a large number of independent, equal-sized tasks to a heterogeneous processor farm. The master processor P 0 can process a task within w 0 time-units; it communicates a task in d i time-units to the i-th slave P i , 1 i p, which requires w i time-units to process it. We assume communication-computation overlap capabilities for each slave (and for the master), but the communication medium is exclusive: the master can only communicate with a single slave at each time-step. We give a

In this paper, we consider the execution of a complex application on a heterogeneous "grid" computing platform. The complex application consists of a suite of identical, independent problems to be solved. In turn, each problem consists of a set of tasks. There are dependences (precedence constraints) between these tasks. A typical example is the repeated execution of the same algorithm on several distinct data samples. We use a non-oriented graph to model...

Porting large parallel applications to new and various distributed computing platforms is a challenging task from a software engineering perspective. The primary aim of this paper is to demonstrate how the development time to port very large applications to the Computational Grid can be signi cantly reduced. TOP-C and AMPIC are software packages that have each seen successful applications in their respective domains of parallel computing and process creation/communication over the Computational Grid. We combined the two packages in one man-week, thereby leveraging several man-years of previous independent software development. As a real world test case, the 1,000,000 line Geant4 sequential application was then deployed over the Computational Grid in three man-weeks by using TOP-C/AMPIC. The cluster parallelization of Geant4 using TOP-C is now included as part of the Geant4 4.1 distribution, and the integration of TOP-C/Ampic and the Globus protocols will additionally enable the use of the fundamental Grid middleware services in the future.

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